Lack of basic services in some areas of the country is one of the major reasons for health workers to migrate to areas with better services but this causes other areas to remain with few health staffs, and resultant poor health care. A simple analysis to determine the availability of these services and at what distance a health worker can access them can be a great solution to policy makers.

Background

Tanzania has been implementing the Primary Health Service Development Program policy to increase the accessibility of health care services to its people at a distance of 5km. Although this policy has been introduced in the various districts in the country, its implementation has been a challenge due to the fact that retaining health workers at these facilities remains problematic. Lack of basic services in some areas of the country is one of the major reasons for health workers to migrate to areas with better services which results in other areas retaining fewer health workers with poorer health care provision in the country. Lack of health care provision in terms of health workers can present barriers to patient’s access to health facilities, who might be forced to travel long distances to access health care. Understanding the problems of health workers, including the availability of basic services in their localities, is important. Distance to basic services has an impact on health worker retention. Travel times, lack of access to transportation, and seasonally inaccessible roadways can present barriers to health workers access to important services such as banks, security or schools for their children.

Objectives

We hypothesized that the lack of basic services was a key factor for health worker migration to other districts that possessed better services. The main goal of this study was to determine the availability and distribution of basic key services that are important to health workers and how close they can be accessed. Specifically, we used spatial analysis to establish a network that considers geographic position and the existence of basic services that could influence the retention of health workers.
In order to achieve this goal we focused on the following questions:
• Is there a reliable water supply source and what is the nearest distance to this source?
• How accessible are the health facility (your place of work)?
• Are there roads which can easily access different routes to services?
• Are there schools that can be accessible by health workers?
• Is there reliable transport (public) that can be used by health workers to move from one point to another?
In particular Euclidian distances from health workers houses to the basic services were computed. Using data on health workers moving in/out of a particular district, we applied multivariate spatial logistic regression to determine the variables that were statistically significant.

Methodology

The study was undertaken in 16 selected district in Tanzania in 2007. Districts were chosen to represent the spatial zonal distribution of the country. Four of the districts (Kigoma Urban, Mtwara Urban, Nymagana and Temeke) were considered urban and the other 12 (Biharamulo, Kilwa, Kondoa, Lushoto, Mafinga, Manyoni, Mbinga, Mpanda, Same, Ulanga, Urambo and Rungwe) were rural districts. Four villages were selected in each district, and three to four health workers houses were selected for inclusion in the mapping exercise. For each district we first assessed all the basic services available, then a hand held Global Positioning System (GPS) was used to map the geographic locations of those services, this includes banks, water source, post office, police post, shops/open market location, schools and bus stops. Other features that located were the health facilities and health worker households. We used ArcGIS 10.1 analysis tools to map the locations of basic services and distances to the nearest services from each health worker household was calculated using simple Euclidean distance. Multivariate logistic regression were used to model the distances to bank, post office, police, schools, referral hospital, water sources and bus stops. Then we compared the significance variables to the data on overall district move in/out of health workers.
Other covariate variables of interest that we included in the spatial logistic model that could be important in our analysis included elevation, rainfall and temperature data.

Results

Main sources of Water: Our results indicated that most of the health worker households and the health facilities they work were not connected to piped water, because there was no such services in the districts. Therefore, health workers are forced to walk long distances (up to 15km) to access water services.
Distance to bank, police and post office services: Most of the health workers in public health facilities collect their salaries through a bank. Our analysis indicated some health workers travel a distance of 160 km to obtain bank, post or police services. This was seen to be a burden especially in those areas where public transport is a major problem (i.e. one bus a day), and that health workers had to take two or three days off, spend nights in a guest house near the bank, and also incur costs for fare and food, which are deducted from their salary.
Accessibility of Health facilities: in most the districts visited during the research, quite large number of health facilities are located near to the roads (0.2 to 3 km). This means that they (in principle) can be accessed by public transport. However, this is not the situation in most of the rural areas where we conducted this research. As mentioned before, public transport is not reliable and the situation is worse during rainy season.
Distance to School: The distance analysis to access schools indicated that schools can be accessed up to 12 km which is also a burden for most children in the rural areas without transport.
Our initial logistic regression models confirmed a statistical significance of distance to basic services and moved in/out of health workers.

Conclusion

In this study we have used GIS and spatial logistic analysis to determine the spatial distribution of existing basic services for health workers. Apart from others, issues like better salary, promotion and various ways of motivating a health worker and understanding the distance to access basic service is important. This is due to the fact that lack of services in some area may cause health workers to migrate to those areas with better services. The application of GIS technology has shown how a health worker can access basic services in terms of travel distance. A multivariate analysis indicated the significance of some variables to the migration of health workers.

Malaria is endemic in most parts of Tanzania and remains a major cause of morbidity and mortality both in rural and urban areas. Ecological niche modelling (ENM) has been considered a useful tool to assess the potential geographical distribution of various species. The application of such tool is very limited in predicting the potential distribution of diseases, especially when using occurrence (presence). In this study an ensemble model approach was employed to predict the current and future (2050) potential distribution of malaria in Tanzania. The ensemble approach demonstrated an enhanced prediction model compared to the individual model outputs.

Background

Malaria is a leading cause of morbidity and mortality accounting for over 30% of the disease burden in Tanzania. Over 95% of the 37.4 million people in the country are at risk of malaria infection. Various factors account for malaria in Tanzania, which include demographic factors, socioeconomic factors, weak health systems, a limited budget, poor governance and accountability, antimalarial drug and insecticide resistance, environmental and climate change, vector migration, and land use patterns. Efforts have been employed to reduce malaria in Tanzania, which include insecticide treated mosquito nets, indoor residual spraying, improved diagnosis by microscopy and rapid diagnostic tests, effective treatment of cases, and implementation of intermittent presumptive treatment of pregnant women. In spite of the many efforts to combat malaria, the disease remains a leading public health problem in most parts of the country. Climate conditions such as precipitation, temperature, and relative humidity have a substantial impact on malaria. Despite the importance of these factors to the distribution of malaria, limited studies have been undertaken to address the association between climatic conditions and malaria epidemics.

Objectives

Previous attempts to map the geographical distribution of malaria have focused on a theoretical model that is based on available long-term climate data, as well as empirical models that fit malaria data to environmental factors to predict the number of months during which transmission is possible. These studies have not demonstrated the predictive ability beyond the input data area. Ecological niche modelling (ENM) has been considered a useful tool to assess the potential geographical distribution of species. It has been applied to diseases to assess the potential distribution of vectors. Applications of ENM to study the distribution of malaria using occurrence cases are limited in Tanzania. Here, we adapt modelling techniques, to predict the current and future potential distribution of malaria. The goals of the study were to (i) identify possible distribution areas of malaria using an ensemble approach that integrate multiple individual models to generate a better and more conservative overall solution, (ii) identify the environmental and climate conditions correlated with malaria occurrences, estimate the population at risk, and (iii) determine how future climate change may affect the distribution of malaria in Tanzania.

Methodology

Data: Malaria occurrence point data were obtained from the Ministry of Health and Social Welfare. These are reported cases from various health facilities in the country. The Current and future (2050) environmental data used in our study were obtained from CliMond gridded climate data, which represents an improvement on the existing global climate data available for bioclimatic modelling. Thirteen environmental variables were used from CliMond; this included eight bioclimatic variables, monthly minimum and maximum temperatures, monthly precipitation, monthly altitude and relative humidity. The 8-bioclimatic variables were mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter, and precipitation of coldest quarter. The study also included other variables such as human population density and normalised difference vegetation index (NDVI). To avoid fitting the model into too many environmental variables, we extracted the environmental information from each presence data and performed a Pearson correlation tests to see if any of the layers were too similar to include in a model together.
Data Processing: The environmental data used for model development were imported into ArcGIS 10.1 software in which they were re-projected to the same coordinate system, clipped to an area encompassing the administrative boundaries of Tanzania, resampled to obtain the same pixel resolution of 5km, extracted to obtain same dimensions, and converted to ASCII format.
Models development: We considered eight modeling algorithms for the ENM development, GAM, GLM, GBM, MAXENT, MARS and RF were implemented in biomod2 package in Revolution R software, SVM using dismo package and GARP using a Desktop GARP.
Ensemble Model Prediction: An ensemble approach was adapted in our study by combining the eight model output through a weighted average using two thresholds (i) the 5th percentile of the training presence (5% TP) and (ii) the least training presence (LTP).
To estimate the populations at risk of malaria, we reclassified the ensemble model outputs to binary maps (which have pixel values of 0 - no malaria and 1 - malaria present) using the two thresholds - 5% TP and LPT. ArcGIS tools were used to compute the population and districts predicted at risk to malaria

Results

The overall contribution of each environmental variable to all the models ranged from 2% to 62%. Population density was the main variables influencing the potential distribution of malaria in all the models. Relative humidity contributed 10.5% to the model followed by altitude (10%) and precipitation of driest quarter (5.4%). The other variables had less influence. The prediction maps revealed that almost the whole country is endemic for malaria. However, the probability of malaria presence varies spatially. All the models depicted high probability (0.5 or greater) of occurrence of malaria in the east and south coast of Indian Ocean, north regions and along Lake Victoria. The models depicted a medium probability of malaria occurrence along the central and west regions. The ensemble model at 5% TP threshold demonstrated high occurrence of malaria in the east, coast of Indian Ocean, north regions and along Lake Victoria, a pattern from east to central, then low occurrence from central to west and also south parts of the country
The ensemble model future (2050) prediction at 5% TP threshold showed an increase/shift of malaria occurrence in the northern part and towards the central part of the country is expected. High percentage of malaria occurrence is predicted in the southern highlands and southern regions of the country. Some areas are predicted with low percentage occurrence in the central regions and areas in the west of the country. Areas in the north, around Lake Victoria and along the coast of Indian Ocean are predicted to maintain the highest percentage of malaria occurrence.
The current population at risk of malaria is estimated to be 29 and 34 million, and this could rise in the future to 81.58 and 93.7 million. About 79% of the districts are at high risk for malaria, which is predicted to increase to 84% in future

Conclusion

A link between climate change and malaria has been described previously; particularly temperature and rainfall are mentioned as the major variables contributing to malaria distribution. The present study, however, shows a lesser contribution of temperature and rainfall in the development of the models, as compared to population density, which depicted the highest contribution. This suggest that (i) population density is the key variable in malaria and (ii) malaria cannot necessary be caused by climate variables, as they may exhibit a smaller role in determining the ecological niche and hence the potential distribution of malaria. However, despite the potential influence of the population variable shown in our model outputs, it is then clear that population density, environmental variables and other factors (than those we used) will need to be included in studies attempting to model malaria endemicity.
Our findings showed high percentage areas predicted by the ensemble for both current and future - 2050, whereas individual models resulted into low predicted areas. The results suggest that ensemble model predictions are more robust than the predictions from individual models.
An important implication of our model is that the predicted distribution of malaria in the various districts in Tanzania can inform the selection of locally appropriate control interventions. The malaria control program can plan better for the distribution of resources by specifically focusing on the areas predicted to be at high risk.